Next Article in Journal
Feature-Level Fusion of Surface Electromyography for Activity Monitoring
Previous Article in Journal
Optimal Scheduling and Fair Service Policy for STDMA in Underwater Networks with Acoustic Communications
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(2), 613; https://doi.org/10.3390/s18020613

An Activity Recognition Framework Deploying the Random Forest Classifier and A Single Optical Heart Rate Monitoring and Triaxial Accelerometer Wrist-Band

1
BioMediTech Institute and Faculty of Biomedical Sciences and Engineering, Tampere University of Technology, 33720 Tampere, Finland
2
Department of Future Technologies, University of Turku, 20500 Turku, Finland
This paper is an extended version of our paper "Human Activity Recognition Using A Single Optical Heart Rate MonitoringWristband Equipped with Triaxial Accelerometer", published in proceedings of the joint conference of the European Medical and Biological Engineering Conference (EMBEC) and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics (NBC), held in Tampere, Finland, in June 2017, pp. 587–590.
*
Author to whom correspondence should be addressed.
Received: 17 January 2018 / Revised: 9 February 2018 / Accepted: 14 February 2018 / Published: 22 February 2018
(This article belongs to the Section Physical Sensors)
Full-Text   |   PDF [348 KB, uploaded 24 February 2018]   |  

Abstract

Wrist-worn sensors have better compliance for activity monitoring compared to hip, waist, ankle or chest positions. However, wrist-worn activity monitoring is challenging due to the wide degree of freedom for the hand movements, as well as similarity of hand movements in different activities such as varying intensities of cycling. To strengthen the ability of wrist-worn sensors in detecting human activities more accurately, motion signals can be complemented by physiological signals such as optical heart rate (HR) based on photoplethysmography. In this paper, an activity monitoring framework using an optical HR sensor and a triaxial wrist-worn accelerometer is presented. We investigated a range of daily life activities including sitting, standing, household activities and stationary cycling with two intensities. A random forest (RF) classifier was exploited to detect these activities based on the wrist motions and optical HR. The highest overall accuracy of 89.6 ± 3.9% was achieved with a forest of a size of 64 trees and 13-s signal segments with 90% overlap. Removing the HR-derived features decreased the classification accuracy of high-intensity cycling by almost 7%, but did not affect the classification accuracies of other activities. A feature reduction utilizing the feature importance scores of RF was also carried out and resulted in a shrunken feature set of only 21 features. The overall accuracy of the classification utilizing the shrunken feature set was 89.4 ± 4.2%, which is almost equivalent to the above-mentioned peak overall accuracy. View Full-Text
Keywords: accelerometer; activity recognition; context awareness;machine learning; photoplethysmography; randomforest; wrist-worn sensors accelerometer; activity recognition; context awareness;machine learning; photoplethysmography; randomforest; wrist-worn sensors
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Mehrang, S.; Pietilä, J.; Korhonen, I. An Activity Recognition Framework Deploying the Random Forest Classifier and A Single Optical Heart Rate Monitoring and Triaxial Accelerometer Wrist-Band. Sensors 2018, 18, 613.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top